October 02, 2025
When I picked up Becoming a Data Head by Alex J. Gutman and Jordan Goldmeier, I quickly realized it’s the book I wish I had read first when starting my data science journey. The authors emphasize a point that often gets lost in technical discussions: data science is only as valuable as the business problem it solves. No algorithm or dashboard matters if the underlying context isn’t clear.
The first step in any data project is understanding what stakeholders actually want. The challenge? Stakeholders often aren’t entirely sure themselves. This creates a guessing game for data scientists and leads to wasted time.
But this principle doesn’t just apply to data professionals — it’s equally important for business leaders and managers reviewing insights. To ensure that data-driven solutions are effective, everyone involved needs a set of questions that clarify the problem and challenge the quality of the data.
Step One: Clarifying the Business Problem
Before beginning any project, it is key to ask meaningful questions that will allow you to accurately define the actual problem the business is facing.
- Why is this problem important to solve?
- Who is directly impacted — which individuals, teams, or customers?
- What happens if we don’t have the right data available?
- How will we know when the project is truly finished?
- What if the results challenge our assumptions or aren’t what we hoped for?
Step Two: Challenging the Quality of the Data
Once you understand the problem, the next step is to ensure the data is ready for analysis. The analysis is only as good as the quality of the data.
- Where does the data come from – who collected it and how?
- Is the data representative of reality?
- Are there biases in the sample?
- Were important observations excluded?
- How were outliers handled?
- What data am I not seeing?
- How were missing values addressed?
- Does this data actually measure what we care about?
Step Three: Asking Smarter Questions About Statistics
Numbers can mislead without context, so it’s important that we really dig into the evidence supporting them.
- What’s the real-world context behind these numbers?
- How large was the sample used to generate them?
- What exactly is being tested here?
- What assumption are we starting with (the null hypothesis)?
- What significance level was chosen, and why?
- How many tests were actually run?
- Can you show the confidence intervals, not just a single number?
- Is the result meaningful in practice, not just statistically?
- Are these results implying causation, or just correlation?
Why These Questions Matter in Practice
During a six-month internship with a large retailer in Germany, I worked on several independent projects within their data science team. At first, I didn’t fully grasp the importance of asking the right questions. As a result, projects often dragged on longer than necessary. I repeatedly had to rework analyses and dashboards because I hadn’t clarified upfront what the project lead truly needed. This back-and-forth wasted time for both me and the stakeholders.
Later, when helping a non-profit improve their analytics capabilities, I approached the project differently. Drawing on what I’d learned from Becoming a Data Head, I began by asking every question I could about their challenges, goals, and data quality. While the project didn’t continue beyond the initial proposal, I left confident that I had built a much stronger foundation — and would have saved significant time if the work had gone forward.
Takeaway for Business Professionals
Whether you’re building data solutions or leading a project, make it a habit to ask these kinds of questions. Doing so, ensures nothing critical is overlooked and that solutions address the right problems from the very beginning.
Asking smarter questions bridges the gap between technical experts and business decision-makers — and positions you as someone who makes insights accessible and actionable.
Conclusion: Connecting Data Science to Business Impact
Data science only makes an impact when it connects to real business needs. That’s what I explore here at AlexDoesData. If you want more practical insights at the intersection of data and business, stay tuned for future projects and blogs.
And if you want to go deeper, I highly recommend Becoming a Data Head by Alex J. Gutman and Jordan Goldmeier — a valuable guide for any professional looking to use data more effectively.